Can Mobile Phones Be Used to Collect Longitudinal Data?

There are two major approaches to data collection with respect to time. Typically, we collect cross-sectional data. This type of data is collected at a single point in time. For example, we might ask someone to complete a single survey. Atypically, we collect longitudinal data. This type of data is collected at multiple points in time, and changes over time are examined. For example, we might collect behavioral data about students today, next week, and the week after that.

The relative advantages and disadvantages of these two approaches can by illustrated by the following similes. Cross-sectional data is like a photograph, whereas longitudinal data is like a video. You might be able to get all the information you need out of a photograph. It does, after all, provide a snapshot of whatever was going on at a particular point in time. But when holding a photograph, you don’t really know what happened before and after that photograph was taken. You must generally assume that what you’re interested in was the same over time. That is not always a safe assumption. Although video solves the timeline problem, it introduces new problems – it’s more complicated to collect (hold that camera still!) and you’ll need to watch the whole thing to figure out what’s going on.

The same is true of cross-sectional versus longitudinal data. Longitudinal data is exceptionally difficult to collect because we usually rely on the kindness of volunteers to complete our studies. When you ask someone to come back to your lab six times, they’re substantially less likely to show up at Time 6 than at Time 1. So that has led researchers to investigate alternate techniques for collecting this type of data. One such approach is to provide mobile phones with pre-installed data collection apps to research participants for long-term use, but little research is available describing how well such an approach should work.

An article in Social Science Computer Review by van Heerden and colleagues[1] sheds some light on this issue. The path from phone purchase to actual data collection in a South African sample is fascinating:

1000 phones were purchased.

996 phones were functional and distributed to research participants over the course of a year.

One month after distribution ended, 734 phones were found to still be accessible of the 996 distributed. Of those missing:

25 had their SIM card changed (making the phone impossible to track and changing its phone number)

32 had deleted the researcher’s data collection software

205 were reported as lost, stolen, broken, or given away

Of the 734 phones, 435 phones were selected at random to be sent two surveys, two weeks apart, via text message.

Of the 435 phones text messaged, 288 were successfully delivered for Survey 1 and 271 for Survey 2. Of those missing:

Thus, of the original 996 distributed phones, 8.4% resulted in complete data. Even considering only the 288 successful messages, the completion rate was 30.9% for Survey 2. That’s pretty depressing.

The researchers actually present this as positive evidence for the use of mobile devices in this manner, considering that meta-analytic evidence suggests a mean expected response rate for web surveys of 34.0% (higher for mail). But considering the extreme expense involved in this approach, I am not convinced. The authors suggest that the next step would be to probe the use of research participant’s own mobile phones, which is certainly a good idea, but I wonder why they didn’t do this in the first place – perhaps they did not expect their population to own mobile phones already.